R: Constant for 'Matrix_eQTL_engine'.

modelANOVA {MatrixEQTL} | R Documentation

## Constant for `Matrix_eQTL_engine`.

### Description

Set parameter `useModel = modelANOVA` in the call of `Matrix_eQTL_main` to indicate that the genotype should be treated as a categorical variable.

### Note

By default, the number of ANOVA categories is fixed to be 3.

To set it to a different number (say, 4) use the following command:

`options(MatrixEQTL.ANOVA.categories=4)`

To check the current settings run:

`getOption("MatrixEQTL.ANOVA.categories", 3);`

### References

The package website: http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL/

### See Also

See `Matrix_eQTL_engine` for reference and sample code.

### Examples

    library("MatrixEQTL")

    # Number of columns (samples)
    n = 100;

    # Number of covariates
    nc = 10;

    # Generate the standard deviation of the noise
    noise.std = 0.1 + rnorm(n)^2;

    # Generate the covariates
    cvrt.mat = 2 + matrix(rnorm(n*nc), ncol = nc);

    # Generate the vectors with single genotype and expression variables
    snps.mat = floor(runif(n, min = 0, max = 3));
    gene.mat = 1 + (snps.mat==1) + cvrt.mat %*% rnorm(nc) + rnorm(n) * noise.std;

    # Create 3 SlicedData objects for the analysis
    snps1 = SlicedData$new( matrix( snps.mat, nrow = 1 ) );
    gene1 = SlicedData$new( matrix( gene.mat, nrow = 1 ) );
    cvrt1 = SlicedData$new( t(cvrt.mat) );

    # name of temporary output file
    filename = tempfile();

    snps1
    gene1

    # Call the main analysis function
    me = Matrix_eQTL_main(
        snps = snps1,
        gene = gene1,
        cvrt = cvrt1,
        output_file_name = filename,
        pvOutputThreshold = 1,
        useModel = modelANOVA,
        errorCovariance = diag(noise.std^2),
        verbose = TRUE,
        pvalue.hist = FALSE );
    # remove the output file
    unlink( filename );

    # Pull Matrix eQTL results - t-statistic and p-value

    fstat = me$all$eqtls$statistic;
    pvalue = me$all$eqtls$pvalue;
    rez = c( Fstat = fstat, pvalue = pvalue)
    # And compare to those from ANOVA in R
    {
        cat("\n\n Matrix eQTL: \n");
        print(rez);
        cat("\n R anova(lm()) output: \n")
        lmodel = lm( gene.mat ~ cvrt.mat + factor(snps.mat), weights = 1/noise.std^2);
        lmout = anova(lmodel)[2, c("F value", "Pr(>F)")];
        print( lmout )
    }

    # Results from Matrix eQTL and "lm" must agree
    stopifnot(all.equal(lmout, rez, check.attributes = FALSE));

[Package MatrixEQTL version 2.2 Index]